Liam
Forum Replies Created
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There could be a few reasons why the Page view event isn’t firing in the iframe. The key thing to note here is that iframes can have their own independent tracking. Try adding a GTM container to the child page that is direct in the iframe. Check if there’s anything in the iframe that is blocking the GTM or GA tags. Make sure that the domain of the iframe page also has been added in the Referral Exclusion list in GA. It might be the case that the GA4 considers the iframe as a new session if it’s not in the list. Following these steps, you should be able to see if the Page view event fires or not. Once you are sure that GTM and GA4 are working properly in the iframe, you would then be able to track the PVs on that child page.
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It’s worth noting that a delay in reporting of data is normal in Google Analytics 4. The delay can sometimes extend to 24-48 hours, especially for more complex or heavy reports. However, standard events are typically visible in reports within a few hours. This is mainly due to the data processing time for analytics. Unfortunately, there’s no way to reduce this delay, as it’s a part of Google’s internal data handling and processing operations. While DebugView provides real-time data for verifying and debugging your implementation, it does not reflect the normal reporting latency in the GA interface. The actual processing latency can depend on various factors such as the volume of incoming data, server load, data quality, etc. So, your setup seems normal and the reporting delay in GA is routine. If the issue continues, I would recommend reaching out to Google Analytics support for more assistance.
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The issue you are experiencing with your custom dimensions in Google Analytics not working might be because you are not properly setting and passing the custom dimension index. In Google Analytics, each custom dimension you create is assigned a unique index that you need to refer to when passing data. In the code you provided, you are setting a ‘unit_id’ value but not passing the custom dimension index from Google Analytics.
Your JavaScript code configuration should include the custom dimension index like this:
gtag('event', 'page_view', {'dimension1': unitId});Make sure to replace ‘dimension1’ with the index of the custom dimension you created in Google Analytics. Also, note that sometimes it can take up to 24-48 hours for data to start appearing on Google Analytics. So, you might want to wait for a day or two if you have just setup your codes.
Lastly, verify that you have correctly added and saved your custom dimension in Google Analytics and that its scope is appropriate for the type of data you’re tracking.
By properly passing the custom dimension index in your code and ensuring your Dimensions configurations in Google Analytics are correct, your custom dimensions should start working.
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It appears you are struggling with stitching together data from numerous schemas in Google’s BigQuery. You attempted to use the UNION ALL command to consolidate the data, but the amount of schemas rendered this approach ineffective. Given the details, it seems that the SQL code you’ve attempted to use is unable to function as anticipated. When dealing with such large amounts of data, it might help to break it down to smaller and manageable subsets for processing. Further troubleshooting or iterative approach might be beneficial. Providing the complexity of the issue, seeking help from specialists or community forums related to Google BigQuery might also be helpful, since they can provide more code specific advice or better strategies to manage huge data schemas.
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You want to create a funnel to trace the origin of visitors who end up downloading your extension and signing up with their email. The roadblock you’re encountering is that you don’t have a unique ID for users to link their data from GA4 to Klaviyo. This makes it difficult to trace back their source (whether it be Twitter, Instagram, etc.) at the time they visited your site and downloaded the extension.
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Sure, to track clicks on specific menu items in your app, you can use Firebase’s Analytics Event Logging feature. You will have to write custom events in the code of your app for every menu item that you want to trace. Whenever a user clicks a particular menu item, the corresponding custom event will trigger and the click will be logged into Firebase. Firebase’s Analytics dashboard will allow you to view these logs and monitor the counts of each event, which reflects the number of clicks on the corresponding menu item. This allows you to track each menu item effectively. It’s important to ensure the custom event code is correctly triggered upon user interaction and that it’s correctly connected to Firebase. You can test the implementation by running a few events to check if they are showing in Firebase Dashboard. If observed, google also provides Firebase SDK documentation that shows a comprehensive manner to log events. Remember, naming the events appropriately relating to the menu item names may make it easier to understand and monitor.
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Yes! To use Google Analytics 4 (GA4) Event Builder, you’ll be dealing with a different setup and format. The Event Builder for GA4 essentially generates a JavaScript command using the gtag.js library, which is different from the image pixel tracking method you were using in the older version of Google Analytics.
Here’s an example of the JavaScript command for tracking a generic event:
gtag(‘config’, ‘GA_MEASUREMENT_ID’, {
‘event_name’: ‘my_event’,
‘event_params’: {
‘parameter_1’: ‘value_1’,
‘parameter_2’: ‘value_2’
}
});In this command, ‘my_event’ is your event name, and ‘parameter_1’ and ‘parameter_2’ are parameters you want to add with the values ‘value_1’ and ‘value_2’ respectively. You typically add these using the GA4 interface and they can be any parameters that are relevant to the event you are tracking.
If you’re attempting to track a conversion event similar to the ‘Always send’ event in Universal Analytics, you’d replace ‘my_event’ with ‘conversion’ and the parameters would be ‘send_to’ with your ‘GA_MEASUREMENT_ID’ in the format ‘GA_MEASUREMENT_ID/conversion’.
It’s important to remember that in GA4, you’ll need to implement this JavaScript command on your confirmation page, different from the previous method where an image tag was embedded. This might require adding it to your page’s HTML or via Google Tag Manager, depending on your set up. Lastly, make sure to test the event to ensure it’s properly tracked in your GA4 interface.
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Unfortunately, as of now, Google Search Console data is not directly integrated with GA4 and BigQuery. While you can link Search Console to Google Analytics and observe data in the Acquisition reports of GA, this data does not directly flow into BigQuery. However, there are other methods to get Search Console data into BigQuery like using third-party tools, APIs, or Google Cloud Functions. So, it has to be done separately and not through GA4. The data you are seeing in BigQuery is most likely just the data coming from GA4. It’s important to understand this distinction to correctly analyze your data.
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Your issue might be related to the permissions of the service account you set up in the Google Cloud Platform console. It could be possible that your service account doesn’t have the proper permissions in Google Analytics to access account data. If you’ve given the service account the necessary permissions temporarily, it might still take some time for permissions to propagate. Make sure that in the Google Analytics Admin settings, under Account User Management, your service account email is listed with “Read & Analyze” permissions at minimum. If the problem insists, you might want to cross-check if the key file is associated with the same account that has been granted the permissions.
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First off, great job on setting up a server side container for your Google Tag Manager with GA4 integration. Now, to validate whether you have properly excluded the ‘ip_override’ parameter, you could use Google Analytics real-time reports or inspect network requests in your browser for a ‘ga’ request that includes the IP parameter. This will help confirm whether IP information is still being sent despite your efforts to exclude it.
As for the ‘_uip’ parameter you noticed in the ‘outgoing HTTP request from server’ section, it essentially serves to override the IP address for the hit and is used for geolocation. If IP anonymization is enabled (which seems to be what you’re aiming for), you indeed should not see the ‘_uip’ parameter containing the IP. However, the ‘_uip’ parameter being present doesn’t necessarily confirm that the IP is being sent and used for geolocation. To ensure that IP addresses are kept anonymous, I would also recommend applying IP anonymization at the platform level and not just on the GA4 tag. That way, you’re not reliant on individual tags for the anonymization.
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Sure, no problem. The issue was with your 5th line of code, but don’t worry – it’s a pretty simple mistake to make. The part of the code where you refer to a ‘child’ of an element, you were using a static value instead of the variable index.
I’ve made the change for you here:
elements[index].children....This will make sure that each child gets a different index rather than the same one. Just a little hiccup that needed to be addressed. Keep at it! You’re doing great!
